Vehicle Platform Research Articles

Hierarchical Lateral Planning With Minimum Curvature Peak for Autonomous Vehicles

Abstract This study focuses on the lateral safety of autonomous vehicles. The aim is to provide greater safety margins for the motion controller under hazardous conditions by improving the planning methods. The proposed lateral planning method employs a hierarchical structure. First, a graph search method that considers road curvature is presented. This method benefits from the design of jump nodes and can generate a safe corridor in real-time by backtracking. Next, a concise and effective optimization problem is constructed in the Frenet frame. It is designed to optimize the path by incorporating the peak, integral, and differential of curvature into the objective function. By solving this problem using CasADi, the path within the safe corridor is obtained. Finally, the proposed planning method is combined with a model predictive motion controller and tested through simulation and experimentation on a real vehicle platform. This planning method has good scalability, adaptability, and real-time performance. Simulation and experimental results show that this method can help avoid exceeding lateral limits of road surface friction, improve path-following accuracy, and enhance the safety of autonomous vehicles in dangerous conditions.

Motion Planning and Control of Autonomous Aggressive Vehicle Maneuvers

Aggressive vehicle maneuvers such as those performed by professional racing drivers achieve high agility motion at the edge of handling limits. These aggressive maneuvers can be used to design human-inspired active safety features for next-generation “accident-free” vehicles. We present a motion planning and control design for autonomous aggressive vehicle maneuvers. The motion planner takes advantages of the sparse stable trees and the enhanced rapidly exploring random tree (RRT*) algorithms. The use of the sparsity property helps to reduce the computational cost of the RRT* method by removing non-useful nodes in each iteration and therefore to rapidly converge to the optimal solution. The proposed motion control design allows the vehicle to operate outside the stability region to accomplish a safe, agile maneuver. A safety region is computed to augment the stability region and the motion control is built on a modified nonlinear model predictive control method. We implement the proposed planner and controller and demonstrate the autonomous aggressive maneuvers on a 1/7-scale racing vehicle platform. Comparison with human expert driver and other existing methods is also presented to demonstrate the performance and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motion planning and control of human driver-inspired aggressive vehicle maneuvers is a challenging task because of high-agility, unstable fast vehicle motions. This paper is motivated by addressing this challenge in autonomous driving technologies. Instead of restricting vehicle motions within a stability region that is taken by existing methods, we augment the conservative stability region to a safety region with guaranteed performance. To improve the computational efficiency of sampling-based motion planners, we take advantage of sparsity and also integration of a nonlinear predictive control method to compute feasible vehicle motion in searching space. The stability of the vehicle motion controller and sub-optimality of the motion planner are analyzed and guaranteed. Using a scaled vehicle testbed, we validate and compare the proposed motion planning and control design with other existing methods and human expert driver. The experimental results demonstrate the superior performance than the other methods and comparable with human expert driving skills.

Study of Proton-Exchange Membrane Fuel Cell Degradation and its Counter Strategies: Flooding/drying, Cold Start and Carbon Monoxide Poisoning

In the context of advancing automotive fleet electrification dynamics, the development of hybrid electric vehicles (HEV) and electric vehicles (EV) serves as a pivotal strategy to mitigate CO2 emissions and promote decarbonization in the transportation sector. While Battery Electric Vehicles (BEV) are prevalent, Fuel Cell Electric Vehicles (FCEV) are gaining traction as a compelling alternative for heavy mobility, particularly Light Commercial Vehicles (LCV) and trucks were relying solely on batteries may not be feasible. Ensuring the efficiency of FCEVs necessitates a profound understanding and control of fuel cell operational conditions. However, concerns persist regarding fuel cell durability due to specific aging phenomena leading to performance decay after operational cycles. The objective of this study is to build a model to accurately characterize and control the fuel cell within a FCEV, by simulating its behavior during cycling and by dealing with common ageing issues like flooding, cold start, and carbon monoxide poisoning. The model described in this study allows not only to simulate the cathode, the anode, and the fuel cell membrane, but it also proposes strategies to handle the wa-ter management at the membrane, deal with cold starts, counter poisoning and, in the end, en-hance the fuel cell performance and lifespan.

Preventing Fraudulent Vehicle Listings: Digital ADS Detection

Abstract: With the increasing popularity of online selling platforms for vehicles, the risk of fraudulent advertisements has also risen significantly. This research paper addresses the challenge of detecting and preventing digital fraud ads, particularly those related to vehicle listings, on typical online vehicle selling web portals. The proposed approach leverages advanced algorithms to identify and filter out fraudulent vehicle ads, including unregistered vehicles and unnecessary spam ads posted by sellers. One of the key techniques employed in this research is pattern matching, which involves comparing the details of posted vehicles with the records from Regional Transport Offices (RTOs) to determine their registration status. By analyzing the vehicle details provided in the ads and cross-referencing them with RTO data, the system can flag unregistered vehicles, thus mitigating the risk of fraudulent listings. Additionally, the study incorporates the use of the You Only Look Once (YOLO) algorithm for object detection in the images accompanying the ads. By applying YOLO, the system can accurately identify vehicles in the images and extract relevant information, such as make, model, and license plate details. This allows for further validation of the authenticity of the listings and helps in filtering out spam ads containing irrelevant or misleading images.

A Comprehensive Review of UAV-Assisted FSO Relay Systems

The evolving requirements of next-generation mobile communications networks can be met by leveraging vertically deployed Unmanned Aerial Vehicle (UAV) platforms integrated with Free Space Optical communications (FSO). This integration offers a flexible and scalable architecture capable of delivering high-rate communication without requiring licenses while aligning with the multi-gigabit paradigm. In recent times, the increasing availability of commercial aerial platforms has facilitated experimental demonstrations of UAV-enabled FSO systems, which play a crucial role in proposed backhaul networks and point-to-point communications by overcoming Line-of-Sight (LOS) challenges. These systems can be rapidly deployed to meet sudden demand scenarios. This document provides a comprehensive review of relevant field demonstrations of UAV-enabled FSO relay systems, with a particular focus on commercially available, free-flying platforms that are driving advancements in this domain. It categorizes the different platforms by considering the operational altitudes of these systems and their payload actuation capacity, which determines their adaptability to variables. The analysis aims to distill the design considerations that lead to optimal performance regarding communications throughput and other relevant metrics. Moreover, it also attempts to highlight areas where design choices have fallen short, indicating gaps in current research efforts toward the widespread adoption of UAV-enabled FSO relay systems. Finally, this work endeavors to outline effective design considerations, guidelines, and recommendations to bridge these identified gaps. It serves as a valuable reference guide for researchers involved in developing UAV-enabled FSO relay systems, enabling them to make informed decisions and pave the way for the successful implementation of such systems.

Flexible Updating of Internet of Things Computing Functions through Optimizing Dynamic Partial Reconfiguration

With applications to become increasingly compute- and data-intensive, requiring more processing power, many Internet of Things (IoT) platforms in robots, drones, and autonomous vehicles that implement neural network inference, cryptographic functions or signal processing (e.g., multimedia, communication), employ field programmable gate arrays (FPGAs). At the same time, dynamic partial reconfiguration (DPR) in modern FPGAs enable changing the function of a part of the FPGA by dynamically loading new bitstreams to the logic regions without affecting the function of other parts of the FPGA. This is especially useful to update functions of IoT devices while in operation for bug fixing or functionality adjustments and, more importantly, when these IoT devices integrate low-cost FPGAs that can hardly realize many hard accelerators. To deal with one of the major limitations of using partial reconfiguration in IoT devices, this work introduces techniques to flexibly use DPR, namely, FLEXDPR, by sharing reconfigurable partitions among different accelerator functions and by supporting virtual relocation of these functions. Experimental results on the Xilinx Zynq-7000 platform reveal energy and latency efficiency improvements of about 20%, on average. Overall, the suggested approach can reduce partial reconfiguration overhead while easing the scheduler’s decisions for the deployment of hardware functions throughout time and space in a performance-conscious manner.

MSFFT: Multi-Scale Feature Fusion Transformer for cross platform vehicle re-identification

A vital component of Intelligent Transportation Systems (ITS) is vehicle re-identification, which allows vehicles to be identified across surveillance devices. Re-identification of vehicles is usually done using information collected from standalone surveillance devices such as fixed surveillance cameras (CCTVs) or aerial devices (UAVs). Re-identifying vehicles across standalone surveillance systems is challenging when there is a severe illumination change, a change of viewpoint, or an occlusion. Cross platform surveillance (CCTV+UAV) based vehicle re-identification is yet to be explored and can mitigate some of the challenges faced during re-identifying vehicles with standalone surveillance systems. This paper proposes a novel cross platform vehicle identification dataset called MCU-VReID using 42 CCTVs and a UAV. A novel re-identification method called Multi-Scale Feature Fusion Transformer (MSFFT) is proposed to re-identify vehicles observed across the cross platform surveillance systems. The network consists of inception layers with transformer networks that enable it to learn the vehicle’s features at a variety of scales. The vehicles observed by two contrasting surveillance systems appear to be transformed representations of one another. Hence a two-stage training approach is facilitated for re-identifying vehicles observed across cross platform surveillance systems. The two-stage training approach aims to learn vehicle semantic transformations in the first stage using self-supervised approaches. The knowledge gained at the first stage relating to vehicle semantic transformations is transferred at the second stage of training to perform re-identification. Extensive experiments using the method demonstrate that MSFFT significantly improves over state-of-the-art methods to perform cross platform vehicle re-identification.

Research Progress of High-Speed Data Transmission Technology for Lightweight and Small-Sized Unmanned Aerial Vehicle Platforms

With the continuous development of science and technology, UAV technology has been widely used in various fields. As an essential part, light and small UAV platforms have the advantages of being small, lightweight, and easy to operate and thus have been widely used in aerial photography, monitoring, rescue, and other fields. However, the traditional data transmission method can no longer meet the demand for the data transmission speed and stability of small and light UAV platforms. Wireless optical communication has the advantages of being high-speed and license-free, as well as having a large bandwidth strong anti-interference ability, and good confidentiality. Therefore, this paper aims to study high-speed data transmission technology for light and small UAV platforms to improve their transmission speed and stability. In this paper, the characteristics and advantages of UAV airborne laser communication are first elaborated to illustrate the advancements and importance of data transmission technology based on light and small UAV platforms. Then, the composition of a UAV airborne laser communication system is clarified to illustrate the feasibility of conducting airborne laser communication research. Then, the current domestic and international research status of people on light and small UAV airborne laser communication high-speed data transmission systems is reviewed, and its key technical features are analyzed. Finally, the prospects for its application and development trend are investigated.

GeoCluster: Enhancing Visual Place Recognition in Spatial Domain on Aerial Vehicle Platforms

GeoCluster: Enhancing Visual Place Recognition in Spatial Domain on Aerial Vehicle Platforms

Predictive Path-Tracking Control of an Autonomous Electric Vehicle with Various Multi-Actuation Topologies.

This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time.

Deep Learning Based Object Detection with Unmanned Aerial Vehicle Equipped with Embedded System

This study aims to introduce an Unmanned Aerial Vehicle (UAV) platform capable of performing real-time object detection and classification tasks using computer vision techniques in the field of artificial intelligence. Previous scientific research reveals the utilization of two different methods for object detection and classification via UAVs. One of these methods involves transmitting the acquired UAV images to a ground control center for processing, whereafter the processed data is relayed back to the UAV. The other approach entails transferring images over the internet to a cloud system, where image processing is conducted, and the resultant data is subsequently sent back to the UAV. This allows the UAV to autonomously perform predefined tasks. Enabling the UAV with autonomous decision-making capabilities and the ability to perform object detection and classification from recorded images requires an embedded artificial intelligence module. The ability of the UAV to utilize image processing technologies through embedded systems significantly enhances its object detection and classification capabilities, providing it with a significant advantage. This enables the UAV to be used more effectively and reliably in various tasks. In the proposed approach, image processing was achieved by mounting a Raspberry Pi 4 and camera on the UAV. Additionally, a Raspberry Pi-compatible 4G/LTE modem kit was used to provide remote intervention capability, and the Coral Edge TPU auxiliary processor was used to increase object detection speed. The TensorFlow Library and the SSD MobilNetV2 convolutional neural network model were used for image processing. During test flights, accuracy values of approximately 96.3% for car detection and 96.2% for human detection were achieved.

Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects

Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.

An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing

This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme.

AI-Embedded UAV System for Detecting and Pursuing Unwanted UAVs

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather.&#x0D; Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications.&#x0D; This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

Prehospital Blood Transfusion: A Cross-Sectional Study of Prehospital and Retrieval Medicine Services across Australia & Aotearoa-New Zealand

Background The frequency and type of prehospital blood product delivery across Australia and Aotearoa-New Zealand is unknown. This study aims to describe transfusion practice across different services in the two nations, as well as identifying potential barriers to the carriage of blood products. Methodology Prehospital and retrieval medicine services operating teams of doctors, specialist paramedics, and/or flight nurses out of specialty bases across Australia and Aotearoa-New Zealand were sent a standardized questionnaire regarding their base characteristics and their current blood transfusion practice. Bases that only performed inter-hospital transfers or search & rescue operations were excluded. Bases were queried on personnel, equipment, transport times, type and volume of blood products carried, frequency of use, and barriers to implementation for those without prehospital blood transfusion programs. Results 64 bases were identified and contacted, of which 5 were excluded and 45 of the remaining 59 (76.3%) responded. 62% (28/45) of respondents routinely carry prehospital blood products. 78.6% (22/28) carried packed red blood cells (PRBC) only, 14.3% (4/28) carried both PRBC and plasma, and 1 service (3.6%) carried whole blood. The mean number of units of blood product carried was 3.3 (SD 0.82). 2 bases (7.1%) carried fibrinogen concentrate. All services carried tranexamic acid and calcium. 734 patients received a blood transfusion in 2021, with trauma being the most common indication (552, 75.2%). Base characteristics varied significantly in staffing, vehicle platform and transfer times. The median transfer time from scene to hospital was 65 min (IQR of 40-92). Services without access to prehospital blood products identified multiple barriers to implementation, including training and supply chain. Conclusion Approximately two-thirds of prehospital services operating advanced teams across Australia and Aotearoa-New Zealand carried blood products and there was wide variation both in the type and number of blood products carried by each base. Multiple barriers to the carriage of blood by all bases were reported, which have implications for service equity. Transfer times are generally long in Australia and Aotearoa-New Zealand, which may impact the generalizability of overseas research performed in prehospital systems with significantly shorter transfer times to services operating in Australia and Aotearoa-New Zealand.

A mini broadband cavity enhanced absorption spectrometer for nitrogen dioxide measurement on the unmanned aerial vehicle platform

We developed a small size and light weight analyzer based on the broadband cavity enhanced absorption spectroscopy for measuring nitrogen dioxide (NO2) with high spatial-temporal resolution and high accuracy. The instrument utilizes a LED centered at 450 nm, a short optical cavity, and a spectrometer to determine the extinction coefficients span 435–465 nm, allowing us to retrieve the mixing ratio of NO2 by absorption cross-section. The measurement precision (1σ) for NO2 is 98 ppt in 2 s under laboratory conditions. The uncertainty is estimated at 6% mainly attributed to cross-section and the mirror reflectivity. Good consistency was achieved by comparing with commercial chemiluminescence detector and cavity attenuation phase shift spectrometer (CAPS) (both R2 = 0.99). We further mounted the instrument on an unmanned aerial vehicle (UAV), and maintained the UAV flight at the height of 20 m, the comparative measurement with a CAPS set in a neighbor building with the same sampling height showed a good correlation (R2 = 0.97), which confirmed the instrumental feasibility and stability on the UAV platform. At last, we present a successful NO2 vertical measurement test, the good performance indicates this portable technique has great potential for vertical measurement. In particular, the mini-CEAS technique can further extend to measure the vertical profiles of other trace gas species (such as nitrous acid) with strong optical structural absorptions, which would largely promote the understanding of atmospheric chemistry on a vertical scale.

Nitric Oxide-Releasing Tubular Polymersomes toward Advanced Gas Therapeutic Carriers.

Nitric oxide (NO) not only plays a vital role in a series of physiological processes but also has great potential for therapeutic applications. One of the existing challenges in using NO as a gas therapeutic is the inconvenience of gaseous NO storage, and thus, it is of importance to develop NO-releasing vehicle platforms. Although a variety of polymer-based NO-releasing nanoparticles have been constructed, a majority of the systems are limited to spherical morphologies. Here we present the preparation of biodegradable NO-releasing amphiphilic block copolymers containing poly(ethylene glycol) (PEG) and poly(trimethylene carbonate-4-nitro-3-(trifluoromethyl)) (PTMC-NF), which can self-assemble into tubular polymersomes. The tubular polymersomes with high aspect ratio structures showed much faster NO-releasing behavior, in contrast to their spherical counterparts under light irradiation. We found that the amount of NO released from tubular polymersomes is 1.5 times that from spherical polymersomes. More importantly, the tubular polymersomes have an enhanced anticancer performance compared to spherical polymersomes, demonstrating that the morphology of the NO-releasing polymersomes has a significant effect on their anticancer ability. In view of the benefits of NO-releasing tubular polymersomes, we expect that they can be used as an efficient NO delivery system for enhanced gas therapy.

Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images.

The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce. We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively. Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.

Cooperative Landing on Mobile Platform for Multiple Unmanned Aerial Vehicles via Reinforcement Learning

Cooperative Landing on Mobile Platform for Multiple Unmanned Aerial Vehicles via Reinforcement Learning